We train a multi-channel speech denoising network (MCSDN) based on the temporal convolutional network (TCN) [1]} in Conv-TasNet [2]} to predict a time-frequency mask \(\mathbf {\mathrm {M}}^{s} \in \mathbb {R}^{T \times F}\)  for the target \(\hat{\mathbf {\mathrm {S}}}\)  from the multi-channel noisy signal \(\mathbf {\mathrm {Y}} \in \mathbb {C}^{M \times T \times F}\) . In order to exploit both the spectro-temporal and spatial information, we concatenate the log power spectrogram of the reference channel signal and inter-channel phase differences (IPDs) between the reference channel and other channels as input features, since IPDs indicate which \(T\) -\(F\)  bins belong to the same directional source in each frequency band. Specifically, we calculate sin(IPD) and cos(IPD) as inter-channel features [3]}, [4]}. The training objective is defined as:
\(\mathcal {L} = |\mathbf {\mathrm {Y}} \odot \mathbf {\mathrm {M}}^s - \mathbf {\mathrm {\hat{S}}}|.\) 
